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<p>Artificial intelligence (AI) has increasingly become a central component of healthcare research, yet a systematic understanding of its scholarly evolution remains limited. This study presents a comprehensive bibliometric analysis of 594 peer-reviewed publications indexed in Scopus between 2012 and April 2024, retrieved using a focused title-based search strategy. Using VOSviewer, we examine publication and citation trends, leading contributing countries and institutions, and thematic structures through keyword co-occurrence networks. Results reveal a marked acceleration in research output after 2018, with the United States, China, and the United Kingdom emerging as dominant contributors and central hubs in international collaboration networks. Keyword analysis indicates a strong methodological emphasis on machine learning, deep learning, and medical imaging, while comparatively limited attention is given to ethical, implementation, and equity-related themes. These findings highlight both the rapid growth and the thematic concentration of AI-in-healthcare research, underscoring the need for future studies to address translational and governance challenges alongside technical innovation.</p>

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Research trends and patterns of artificial intelligence in healthcare using bibliometric analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月30日

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  • Abdulaziz Yasin Nageye,
  • Abdukadir Dahir Jimale,
  • Mohamed Omar Abdullahi,
  • Mohamed Abdirahman Addow,
  • Isse Farei Disow

摘要

Artificial intelligence (AI) has increasingly become a central component of healthcare research, yet a systematic understanding of its scholarly evolution remains limited. This study presents a comprehensive bibliometric analysis of 594 peer-reviewed publications indexed in Scopus between 2012 and April 2024, retrieved using a focused title-based search strategy. Using VOSviewer, we examine publication and citation trends, leading contributing countries and institutions, and thematic structures through keyword co-occurrence networks. Results reveal a marked acceleration in research output after 2018, with the United States, China, and the United Kingdom emerging as dominant contributors and central hubs in international collaboration networks. Keyword analysis indicates a strong methodological emphasis on machine learning, deep learning, and medical imaging, while comparatively limited attention is given to ethical, implementation, and equity-related themes. These findings highlight both the rapid growth and the thematic concentration of AI-in-healthcare research, underscoring the need for future studies to address translational and governance challenges alongside technical innovation.